All these methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} had a trade-off between computation and performance; the methods with high performance usually require expensive computations. Also, they only applied standard CNNs with static scale representation for feature extraction. In this work, we aim to learn dynamic scale representation for more accurate feature matching. We also estimate pixel-wise visibility information to remove noises and wrong matching pixels in cost aggregation. These goals are achieved by analyzing the normal curvature of the image surface.
